Data Package Metadata   View Summary

Macrosystems EDDIE Module 9: Using High-Frequency Data to Improve Water Quality (Instructor Materials)

General Information
Data Package:
Local Identifier:edi.1124.1
Title:Macrosystems EDDIE Module 9: Using High-Frequency Data to Improve Water Quality (Instructor Materials)
Alternate Identifier:DOI PLACE HOLDER
Abstract:

This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 9: Using High-Frequency Data to Improve Water Quality, a ~3-hour educational module for undergraduates. In recent decades, there have been substantial improvements in our ability to monitor water quality in real time using sensors that measure variables at a high frequency (e.g., every few minutes). These high-frequency data have tremendous potential to inform drinking water management by providing real-time information to water treatment plant operators regarding water quality. Moreover, in addition to directly informing water management decision-making, collection of high-frequency water quality data has enabled recent development of water quality forecasts, or predictions of future water quality conditions with uncertainty. Often, water quality forecasts are developed with the goal of informing and improving water treatment and management by giving managers a pre-emptive warning about potential water quality impairment. To introduce water treatment students to use of high-frequency data and forecasts to improve water quality, we developed a short (one- to three-hour) module which develops key skills in high-frequency data and forecast visualization and interpretation that are applied to drinking water treatment scenarios, using data from the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) program. This module was developed as part of a virtual, asynchronous curriculum for community college students training to become drinking water treatment plant operators, and could also be taught in high school or introductory undergraduate environmental science and natural resource management courses. The module is designed to be flexible for use in-person, hybrid, and virtual, asynchronous course formats. Students complete module activities using an R Shiny web application which can be accessed from an internet browser on a computer. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module9/. A GitHub repository is available for the R Shiny application code (https://github.com/MacrosystemsEDDIE/module9), the code repository has been published with a DOI to Zenodo (ZENODO DOI). This data package includes open source versions of module slide decks, a student handout, and an instructor manual which can be used to teach the module. Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module9.html).

Publication Date:2024-08-28
For more information:
Visit: DOI PLACE HOLDER

Time Period
Begin:
2024-02-22
End:
2024-08-28

People and Organizations
Contact:Carey, Cayelan C. (Virginia Tech) [  email ]
Creator:Lofton, Mary E. (Virginia Tech)
Creator:Cooke, Rosa-Lee (Mountain Empire Community College)
Creator:Carey, Cayelan C. (Virginia Tech)

Data Entities
Other Name:
instructor_materials
Description:
This zip folder contains materials for instructors to teach the Macrosystems EDDIE module in their classroom. See README file for file types and descriptions
Detailed Metadata

Data Entities


Non-Categorized Data Resource

Name:instructor_materials
Entity Type:unknown
Description:This zip folder contains materials for instructors to teach the Macrosystems EDDIE module in their classroom. See README file for file types and descriptions
Physical Structure Description:
Object Name:instructor_materials.zip
Size:4335436 bytes
Authentication:9908b1d957e9c9e66e28636b5de6ec19 Calculated By MD5
Externally Defined Format:
Format Name:application/zip
Data:https://pasta-s.lternet.edu/package/data/eml/edi/1124/1/a24794498ee9638a12c2844de7c5daf5

Data Package Usage Rights

This information is released under the Creative Commons license - Attribution - CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these data ("Data User" herein) is required to cite it appropriately in any publication that results from its use. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplicate publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. Where appropriate, the Data User is encouraged to consider collaboration or co-authorship with the authors. The Data User should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is." The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. Thank you.

Methods and Protocols

These methods, instrumentation and/or protocols apply to all data in this dataset:

Methods and protocols used in the collection of this data package
Description:

MODULE DEVELOPMENT AND TESTING

Module teaching materials were developed by M.E. Lofton, R.L. Cooke, and C.C. Carey to provide instructors with a ready-to-use, adaptable module that could be implemented in a 1-3 hour time period to introduce students to use of high-frequency data and water quality forecasts in drinking water management and treatment.

As the ninth module within the suite of Macrosystems EDDIE (www.macrosystemseddie.org) teaching materials, this module was developed to introduce students to use of high-frequency water quality data and forecasts to inform drinking water treatment. In this module, students will explore high-frequency water quality data from a drinking water reservoir and use these data to make decisions about water extraction depth and treatment. This module will teach students to interpret high-frequency water quality data; how water quality variables change over time in reservoirs located in temperature regions; and how water quality data and forecasts can be used to inform water treatment decision-making.

The specific student learning goals for this module are that by the end of the module, students will be able to:

- Define key measures of surface freshwater quality (water temperature, dissolved oxygen, and turbidity).

- Explain how water temperature changes over the course of a year in a temperate reservoir and how these changes affect water quality.

- Interpret high-frequency water quality data to make decisions about water extraction depth for a drinking water reservoir.

- Evaluate water quality data and forecasts to make decisions about drinking water treatment.

The module was tested by Cooke with community college students during the 2024-2025 academic year, and student feedback during this testing period was used to update and optimize teaching materials.

MODULE WORKFLOW

Workflow for this module:

1. Instructor chooses whether to deliver the module using Canvas or not. If using Canvas, the instructor should import the module to their course from the Canvas commons (https://lor.instructure.com/resources/15a6383a448e49a99dcdbb8c029a2a31?shared). If not using Canvas, all module materials can be accessed from this web page: https://serc.carleton.edu/eddie/teaching_materials/modules/module9.html.

2. Students view a short (~10 minute) introductory video either in Canvas or in the R Shiny web application. The same video is linked from both locations. Alternatively, instructors may choose to modify the introductory presentation and present it themselves. An editable version of the slides is provided with the teaching materials below.

3. Students navigate to the R Shiny web application and follow the workflow instructions outlined on the Introduction tab:

a. Watch the introductory presentation provided in Canvas and embedded in the interactive R Shiny web application if you have not already done so.

b. Watch the "Guide to Module" video embedded in the interactive R Shiny web application to learn about key features of the module that will help you complete module activities and answer questions. Optionally, you can also go through the "Quick-start" guide to the module using the button at the top right corner of the module web page.

c. Select a focal reservoir.

d. Open the Canvas quiz questions associated with the reservoir you have chosen OR if you are not using Canvas, download a copy of all the questions as a Word document by clicking the "Download student handout" button.

e. Work through the module to complete the Introduction questions and Activities A, B, and C in this web app. When you are prompted to answer questions, enter your answers in the Canvas quiz. Be sure to fill in the Canvas quiz that corresponds to the reservoir site you have chosen! If you are not completing the module using Canvas, you may type your answers into the Word document.

f. If you would like to take a break and come back later, or if you lose internet connection, all you have to do is re-load this web app, re-select your reservoir site in the Introduction, and you will be able to resume your progress. On Canvas, you can save your quiz responses using the "Save" button. In Word, you can save your answers in the document on your computer.

g. When you have finished the module activities, be sure to submit your Canvas quiz for grading. If you are completing the module by answering the questions in a Word document, be sure to submit the document to your instructor for grading.

4. The instructor can choose how to assess student module question responses. If using the Canvas quiz, all self-grading questions are worth 1 point by default, and short answer questions are worth 0 points. The point values may be adjusted by the instructor. If using the Word document, point values are not assigned to questions; this is left to the discretion of the instructor.

For more information, we refer users to the website and R Shiny application for the module listed below.

WEBSITE & R SHINY APPLICATION

Lofton, M.E., Cooke, R.L., and Carey, C.C. 2024. Macrosystems EDDIE Module 9: Using High-Frequency Data to Improve Water Quality. https://serc.carleton.edu/eddie/teaching_materials/modules/module9.html.

Lofton, M.E., Cooke, R.L., and Carey, C.C. 2024. Macrosystems EDDIE Module 9: Using High-Frequency Data to Improve Water Quality. (R Shiny application) (v1.0). Zenodo. DOI XXX.

People and Organizations

Publishers:
Organization:Environmental Data Initiative
Email Address:
info@edirepository.org
Web Address:
https://edirepository.org
Id:https://ror.org/0330j0z60
Creators:
Individual: Mary E. Lofton
Organization:Virginia Tech
Email Address:
melofton@vt.edu
Id:https://orcid.org/0000-0003-3270-1330
Individual: Rosa-Lee Cooke
Organization:Mountain Empire Community College
Email Address:
rcooke@mecc.edu
Individual: Cayelan C. Carey
Organization:Virginia Tech
Email Address:
Cayelan@vt.edu
Id:https://orcid.org/0000-0001-8835-4476
Contacts:
Individual: Cayelan C. Carey
Organization:Virginia Tech
Email Address:
Cayelan@vt.edu
Id:https://orcid.org/0000-0001-8835-4476

Temporal, Geographic and Taxonomic Coverage

Temporal, Geographic and/or Taxonomic information that applies to all data in this dataset:

Time Period
Begin:
2024-02-22
End:
2024-08-28
Geographic Region:
Description:The Department of Biological Sciences at Virginia Tech is located in Blacksburg, Virginia, USA
Bounding Coordinates:
Northern:  37.229596Southern:  37.22854
Western:  -80.426228Eastern:  -80.424863

Project

Parent Project Information:

Title:Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management
Personnel:
Individual: Cayelan C. Carey
Organization:Virginia Tech
Email Address:
Cayelan@vt.edu
Id:https://orcid.org/0000-0001-8835-4476
Role:Principal Investigator
Funding: National Science Foundation EF 2318861

Maintenance

Maintenance:
Description:Ongoing
Frequency:

Additional Info

Additional Information:
 

AUTHORSHIP CONTRIBUTIONS: MEL, RLC, and CCC conceptualized the module. MEL drafted and revised all module materials with substantial feedback from RLC and CCC.

Other Metadata

Additional Metadata

additionalMetadata
        |___text '\n      '
        |___element 'metadata'
        |     |___text '\n         '
        |     |___element 'emlEditor'
        |     |     |___text '\n            '
        |     |     |___element 'app'
        |     |     |     |___text 'EMLassemblyline'
        |     |     |___text '\n            '
        |     |     |___element 'release'
        |     |     |     |___text '3.5.5'
        |     |     |___text '\n         '
        |     |___text '\n      '
        |___text '\n   '

EDI is a collaboration between the University of New Mexico and the University of Wisconsin – Madison, Center for Limnology:

UNM logo UW-M logo